Questions tagged [topic-model]

A topic model describes text from a large corpus as a probability distribution over topics which are probability distributions over words. There are quantified contributions from all topics to a specific text.

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Gensim LDA model: return keywords based on relevance (λ - lambda) value

I am using gensim library for topic modeling, more specifically LDA. I have created my corpus, my dictionary and my lda model, and with the help of pyLDAvis library I visualize the results. When I ...
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1k views

why the accuracy of LDA model is always changing and also is high

Let’s explain the whole goal firstly, then go through the question. I am using topic modeling like LAtent Dirichlet Allocation and NMF to extract the topic from a collection of documents. My dataset ...
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1answer
259 views

Automatic topic labelling for topic modelling

I am just curious to know if there is a way to automatically get the lables for the topics in Topic modelling. It would be really helpful if there's any python implementation of it.
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Topic modelling on only 24 documents gives the same “topic” for any K

Description: I have 24 documents, each one of around 2.5K tokens. They are public speeches. My text preprocessing pipeline is a generic one, including punctuation removal, expansion of English ...
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125 views

Extending Author-Topic LDA

I've been trying to extend the LDA and wanted some help, direction and insight. Can Author-Topic LDA be used as a document "category" model? The premise of the Author-Topic model is that multiple ...
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9 views

Best measure to indicate quality of LDA model

On my corpora, I am running LDA with different settings (I experiment with different number of topics, different different ngrams and TFIDF or regular BOW). Now, I want to rank these setups to select ...
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29 views

Self-supervised learning for automatic labeling of data using LDA and Word2Vec

I am trying to implement this paper A Brand-New Look at You: Predicting Brand Personality in Social Media Networks with Machine Learning for labeling Twitter data of brands with a corresponding brand ...
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1answer
27 views

How to compare topics generated from topic modeling from different datasets?

I have two datasets of a similar theme. Let's assume Dataset A and Dataset B. Using the top2vec model (https://github.com/ddangelov/Top2Vec) (https://arxiv.org/abs/2008.09470) on each dataset, I came ...
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1answer
40 views

Hierarchical dirichlet process results

I am thinking about using hierarchical dirichlet process to model a patent dataset. I've seen that HDP uses a base distribution and assumes that every topic comes from that base distribution. The ...
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61 views

Trying to use term document matrix as input to orange 3

I have a CSV file that has tokens as columns and documents as rows, where the rest of the cells are ints that represent term frequency. I'm trying to use this as input into Orange 3, but Orange 3 ...
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17 views

Searching for ressource recommendations (books,papers) to validate the results of a Topic Model (LDA)?

Hi I am building a Topic Model Process with Python. To do this I am using the LDA. However I am having trouble to determine the ideal amount of topics. Currently I am using the CoherenceModel ...
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107 views

Latent Dirichlet Allocation in R, topicmodels using VEM algorithm or Gibbs Sampling mixing tm and topicmodels library or WarpLDA from text2vec?

If I am trying to classify 230k text abstracts, which option would be better and more precise when aplying LDA?
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19 views

Determining topic of text

I was wondering what I should be looking into if I want to measure the similarity between a paragraph and a corpus of text. For example, given a paragraph of text and the entire corpus of Data ...
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1answer
45 views

Which approach to select category based on keywords

I want to assign a certain category to a group of keywords. So i.e. people can upload images or videos, when they do this they can set keywords for this. These keywords are free to type so words can ...
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1answer
41 views

Classify documents using a set of known vocabularies

I have a bunch of documents that I want to classify which ones talk about soccer (unsupervised learning, I do not want to manually label the documents). One way I am thinking about is to go online ...
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13 views

How to find out the subject of an email (in the form of a sentence) or a pdf document in NLP using Python

How to find out the subject of an email (in the form of a sentence) or a pdf document in NLP using Python. If I do topic modelling and get different groups of topic, how do I pick out the only topic ...
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1answer
46 views

How to build News Tagging model(s)

I am trying to build a news tagging system. Given a piece of news article, find 5-6 key terms from the news article that best describe the article. Refer to the image below from google news. What are ...
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103 views

Performance Metric for topic extraction when there is no ground truth

I am extracting topics from text using a predefined ontology containing 2690 concepts, wordnet(to expand concept terms with their synsets, and other morphological forms of the same word) and lucene to ...
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723 views

Perplexity increasing on Test DataSet in LDA (Topic Modelling)

I was plotting the perplexity values on LDA models (R) by varying topic numbers. Already train and test corpus was created. Unfortunately, perplexity is increasing with increased number of topics on ...
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1answer
2k views

How to map topic to a document after topic modeling is done with LDA

Is there any way I can map generated topic from LDA to the list of documents and identify to which topic it belongs to ? I am interested in clustering documents using unsupervised learning and ...
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1answer
370 views

Hellinger Distance in Gensim

I have set of documents as follows where each document has set of words that represents the content of it. ...
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156 views

What is “energy spectrum” in Latent Semantic Indexing (LSI)?

What is meant by energy spectrum in LSI(Latent Semantic Indexing)? I am doing topic modeling with gensim's LsiModel, and part of the output per chunk is the following: ...
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2answers
444 views

Memory error - Hierarchical Dirichlet Process, HDP gensim

I am running Hierarchical Dirichlet Process, HDP using gensim in Python but as my corpus is too large it is throwing me following error: ...
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2answers
16 views

Best Python NLP library for supervised topic classification

I have a labeled dataset that I have ingested into a dataframe. It consists of news articles, ...
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41 views

Cluster images labels in some given categories using word embeddings

Given: set of images Labels in string format each one. Also I've given a set of Categories, also in string. ($Images \neq Categories $) Goal: I need to map given labels to given categories to "...
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5 views

Simple approach to assign topic / heading to a text

I have an uncleaned text field something like Apple Juice xxx Newyork .. Store and a assigned topic Juice Centre. What are ...
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38 views

How to measure the Optimal-number of Topics & Topic-Coherence score in Orange's LDA Topic-Modeling?

I'm trying to build a suitable "code-free" Topic Model in "Orange Data Mining" software for the experimental part of my Thesis, in order to analyze the themes and trends of ...
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4 views

Vector dimensionality seems to be implemented incorrectly

I'm trying to implement a fuzzy topic modeling approach in Python based on a paper, which is accommodated with an R implementation from GitHub. In one of the first steps a document term matrix is ...
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13 views

How does LDA model do inference on new documents?

Through Latent Dirichlet Allocation (LDA) one can learn topics from texts. The trained topic model can then be used on unseen data to infer to what extent each of the learned topics are represented in ...
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22 views

Extract keywords from set of similar sentences by using python

I have a list of similar sentences, and I would like to automatically extract top-n important keywords [single word length] from a whole set of those sentences by using python. These sentences are ...
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17 views

Topic modelling with many synonyms - how to extract 'latent themes'

Here's my corpus ...
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28 views

Use KL divergence to label topics from LDA

I have used sklearn's sklearn.decomposition.LatentDirichletAllocation module to model 10 topics in a set of documents. I have also used the same on a reference text (1 document) and obtained a 1 topic ...
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1answer
23 views

Twitter Data-Analyse: What can I do with the data?

I retrieve data to a specific topic from Twitter and did my sentiment analysis on it. I never did anything in NLP, etc. So what else can I do with that? "Main goal" would be to find out if ...
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1answer
24 views

Which tool is good to collect tweets on 50 keywords over the last 5 years and then analyze them with the LDA algorithm or sentiment analysis?

I want to find tweets from the last 5 years to a topic. For this I decide for 50 Keywords (related to the main topic), where I want to find data on Twitter. I want to find out how the trend on the ...
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19 views

How to improve text classification using topic modeling feature vector?

I have a binary text classification problem. I am using the topic model(LDA) trained on Wikipedia to get the feature vectors to classify documents. I have tried Logistic Regression, Random Forest, ...
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2answers
116 views

Topic models for non-textual data?

I am looking to employ an unsupervised clustering on a dataset where each observation has a mix of textual and non-textual features. For each observation, I combine the features into a single vector ...
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39 views

Topic Modelling

New to python - topic modelling, trying to include bigrams in preprocessing Had done the following, ...
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54 views

Linking LDA topics to the input documents

I am new to LDA topic modelling. I am using gensim and am able to generate topics that make sense. Using 25k of documents, I can also print them using print_topics. ...
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1answer
131 views

Topic modelling or simple case of calculating probabilities?

I am trying to find the common topics between articles read using the respective tags attached to each article. Background of my mini project: The problem I am trying to solve involves looking at ...
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1answer
642 views

Using Topic Models in R

I am learning about Probabilistic Topic Models by reading this article by D. Blei, watching this video, and doing this exercise A Gentle Introduction to Topic Modeling in R. After the topics in my ...